Key result
A classifier using a subset of features derived from physiological signals correctly classified 3 levels of user enjoyment in a car racing game with a 57% accuracy rate.
A classifier using physiological signals and a genetic algorithm can recognize 3 levels of enjoyment in a car racing game with 57% accuracy.
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Supports physiological classifiers for user experience assessment; leaves open validation and accuracy thresholds for practical use.
In this paper we present a case study on The Open Racing Car Simulator (TORCS) video game with the aim of developing a classifier to recognize user enjoyment from physiological signals. Three classes of enjoyment, derived from pairwise comparison of different races, are considered for classification; impact of artifact reduction, normalization and feature selection is studied; results from a protocol involving 75 gamers are discussed. The best model, obtained by taking into account a subset of features derived from physiological signals (selected by a genetic algorithm), is able to correctly classify 3 levels of enjoyment with a correct classification rate of 57%.
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Tognetti et al. (2010) studied Video game players (n=75). Physiological signal classification model was evaluated on Correct classification rate of 3 levels of enjoyment. A classifier using a subset of features derived from physiological signals correctly classified 3 levels of user enjoyment in a car racing game with a 57% accuracy rate.
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